IEEE SIG on Artificial General Intelligence, Models, and Agents (AGILE) has been formally approved by IEEE Communications Society (ComSoc) Technical Committee on Big Data (TCBD). Dr. Li serves the founding chair.

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Official Link

Officers

Chair: Rongpeng Li, Zhejiang University, China, lirongpeng AT zju.edu.cn
Vice Chair: Biao Zhang, Google DeepMind, UK, biaojiaxing AT google.com
Vice Chair: Lan Zhang, Clemson University, USA, lan7 AT clemson.edu
Vice Chair: Charilaos Zarakovitis, Axon Logic, Greece, c.zarakovitis AT axonlogic.gr

Advisor: Dusit Niyato, Nanyang Technological University, Singapore, dniyato AT ntu.edu.sg
Advisor: Honggang Zhang, honggangzhang AT zju.edu.cn
Advisor: Jun Zhang, Hong Kong University of Science and Technology, China, eejzhang AT ust.hk
Advisor: Mehdi Bennis, University of Oulu, Finland, mehdi.bennis AT oulu.fi
Advisor: Xianfu Chen, Shenzhen CyberAray Network Technology Company Ltd, China, xianfu.chen AT ieee.org
Advisor: Yan Zhang, University of Oslo, Norway, yanzhang AT ieee.org
Advisor: Yonghui Li, University of Sydney, China, yonghui.li AT sydney.edu.au
Advisor: Yusheng Ji, National Institute of Informatics, Japan, kei AT klab.nii.ac.jp

Scope and Objectives

Artificial General Intelligence (AGI) is an implicit or explicit north-star goal since 1956 Dartmouth AI Conference. Given the rapid advancement of Machine Learning (ML) models, the concept of AGI has passed from being the subject of philosophical debate to one with near-term practical relevance. Nowadays, benefiting from the rapid progress and astonishing success in Natural Language Processing (NLP) and Computation Vision (CV), “sparks” of AGI are even regarded to be already present in the latest generation of Large Language Models (LLMs) and Large Vision Models (LVMs), with prominent examples like ChatGPT, Gemini, DALL-E and Sora. Meanwhile, techniques like generative Generative Adversarial Networks (GANs) and Diffusion models as well as scalable Transformers not only boost the arrival of these amazing Foundation Models (FMs), but also are seen as a transformative technology beyond shaping the AI field:

  • FMs promise a tangible enhancement to wireless communications and networks by leveraging the generative capabilities as well as the multimodality nature of the data acquired in wireless networks. It promises to overcome long-standing difficulties such as low generality, limited performance gain, complicated
    management, and inconvenient collaboration.
  • The application of FMs for inference and decision-making purposes have demonstrated appealing results. It is widely anticipated that FM-empowered (connected) autonomous agents with embodied intelligence are expected to emerge with the astonishing capabilities of accomplishing tasks autonomously and coherently.

Given these facts and visions, there is a clear need to establish a Special Interest Group (SIG) on AGI, Models, and Agents (AGILE) to address the emerging technical challenges therein. On one hand, it still requires ongoing significant efforts to deliver cost-effective AGI solutions. On the other hand, how to tackle the bloated parameters in FMs in edge and user equipment remain under-investigated.

This SIG aims to organize and solicit researchers from both the academia and the industry to accelerate the study on AGILE. Tentative topics include, but are not limited to

  • Artificial general intelligence techniques for AGILE
  • Model design and training for AGILE
  • Communication techniques in AGILE
  • Communication and learning theory in AGILE
  • Performance evaluation metrics of AGILE
  • Collaboration mechanism in AGILE
  • Network architecture for AGILE
  • Security and privacy of AGILE
  • Data collection and governance of AGILE
  • Full-lifecycle management and orchestration of AGILE
  • Architecture and protocol design & standarization of AGILE

Founding Members

  • Changyang She, University of Sydney, Australia, changyang.she AT sydney.edu.au
  • Chengchao Liang, Chongqing University of Posts and Telecommunications, China, liangcc AT cqupt.edu.cn
  • Chenghui Peng, Huawei Technologies, China, pengchenghui AT huawei.com
  • Chungang Yang, Xidian University, China, chgyang2010 AT 163.com
  • Dong Wang, China Telecom, China, wangd5 AT chinatelecom.cn
  • Guangxu Zhu, Shenzhen Research Institute of Big Data, China, gxzhu AT sribd.cn
  • Gang Feng, University of Electronic Science and Technology, China, fenggang AT uestc.edu.cn
  • Haijun Zhang, Beijing Science and Technology University, China, zhanghaijun AT ustb.edu.cn
  • Jihong Park, Deakin University, Australia, jihong.park AT deakin.edu.au
  • Kai Yang, Beijing Institute of Technology, China, yangkai AT bit.edu.cn
  • Qiang Liu, University of Nebraska-Lincoln, USA, qiang.liu AT unl.edu
  • Qimei Chen, Wuhan University, China, chenqimei AT whu.edu.cn
  • Sai Mounika Errapotu, University of Texas at El Paso, serrapotu AT utep.edu
  • Shangmin Guo, University of Edinburgh, UK, s.guo AT ed.ac.uk
  • Xinyue Zhang, Kennesaw State University, USA, xzhang48 AT kennesaw.edu
  • Xueli An, Huawei Technologies, Germany, xueli.an AT huawei.com
  • Yong Xiao, Huazhong University of Science and Technology, China, yongxiao AT hust.edu.cn
  • Yuanming Shi, Shanghai Tech University, China, shiym AT shanghaitech.edu.cn
  • Yao Sun, University of Glasgow, UK, Yao.Sun AT glasgow.ac.uk
  • Yixiong Wei, Zhejiang Lab, China, yx_wei AT zhejianglab.com
  • Zhi Liu, The University of Electro-Communications, Japan, liuzhi AT uec.ac.jp
  • Zhijin Qin, Tsinghua University, China, qinzhijin AT tsinghua.edu.cn